Most currently used navigation map building algorithm is SLAM algorithm, such as grid map algorithm and topological map algorithm, the environmental feature point is collected by the wide-angle lens to build navigation map, it demands high quality for visual signals, image processing algorithms, and hardware performance. However, grid division and topological point collection are mainly finished by man-made setting, it only suitable for the specific static environment and hard to an extension. With the continuous complex of current motion environment, especially in dynamic condition, single navigation method is not satisfied with actual requirements, multi-navigation strategy will complement advantages of kinds of navigation methods to get better effect, the usually used data fusion mode is Kalman filtering algorithm, however, Kalman filtering algorithm needs to build motion model and observation model of system precisely, complex motion environment modeling is very complex in calculation, it restricts the application of Kalman filtering.
According to the mechanism of the biocognitive environment to make robot build environment cognitive map more intelligent, many drawbacks of currently used environment map building algorithm can be solved. Mammals (such as rat and human) need to encode for the environment when they move in it purposefully; it needs inner fusion for various sensor information, the inner neural expression is formed, this inner neural expression called cognitive map. Spatial navigation cell relates to environment cognition, and cognitive map building in the hippocampal structure are head direction cell, stripe cell, grid cell, border cell and spatial cell.
Hippocampus is the key encephalic region for animal environment cognition. In 1971, O'Keefe and Dostrovsky found that pyramidal neurons of region CA1 and CA2 in hippocampus only discharge in specific spatial position, when an animal is in a specific position, pyramidal neurons has the highest discharge frequency, when it goes away from this position, discharge frequency is decreased, these neurons is called place cell, animal activity range in environment relates to its discharge activity is called place field. Reflection relationship between brain and external environment is formed since selective discharge of place cell; it is a key factor for animal self-orientation and spatial environment cognition. More precisely, place cell has following properties:
Place field of place cell is generated swiftly when animal turns into a new environment; it will cover the whole environment through the traversal of environment;
Same position cell may issue in different environment and has different place field;
Different with neurocyte on visual cortex, specific position of place cell in brain has no relationship with its corresponding place field, in another word, place field of specific geographic position corresponding to two adjacent place cells may be not adjacent;
Exogenous information (such as visual, smell) and endogenous information (such as vestibule and body) can lead place cell to issue, and static place field is formed, place cell can also issue and form static place field without exogenous information (such as dark environment).
In 1990, Taube found a kind of head direction orientation neuron in back subicular, when animal's head heads to a specific direction, this kind of neuron has maximum discharge, it is called head direction cell, it is a kind of dependent head direction neuron, its discharge activity only relates to head direction in horizontal plane, it has no relation to the position, posture and actions if animal. Each head direction cell has only one optimum direction, in a fixed environment, head direction cell can contain steady state in a long time. It can be expressed as a gauss model.
In 2005, Hafting found grid cell which discharges strongly to specific spatial position through changing the shape and size of test chamber, when rat moves in two-dimensional space, regularly repeatable discharge of grid cell occurs in specific position, this spatial range is called grid field, triangle evoked set which is formed by connecting multiple grid cells discharge region, it covers whole spatial environment that the rat has passed. Spacing between two grid nodes is about 39-78 cm, spacing of grid firing field on dorsoventral side-axis along the entorhinal cortex is increasing gradually, usually, evoked set of same grid cell in different environment is different, when the rat is in dark environment, grid set is steady; there are 4 basic feature of grid formed by grid cell: {circle around (1)} spacing: distance between the centers of firing fields; {circle around (2)} orientation: angularity versus external reference coordinate; {circle around (3)} phase: displacement in x-axis and y-axis versus external reference point; {circle around (4)} size of firing field: spatial range of grid cell discharging. These 4 elements are a spatial parameter of grid map.
In 2012, O'Keefe proved that there exist periodical stripe firing field in parasubicular cortex and entorhinal cortex, it is called stripe cells, its' firing field covers the whole spatial environment in a parallel stripe shape. Stripe cells is considered as basic mechanism to finish path integral, its' firing activity can be characterized by four characters: {circle around (1)} stripe spacing: central distance between two stripe firing field; {circle around (2)} stripe field width: spatial horizontal discharge range of firing field; {circle around (3)} stripe phase: displacement verse external reference point; {circle around (4)} preferred direction: angularity of stripe field verse external reference coordinate system;
Activation rate of these five kinds of the spatial cell is shown in FIG. 2.
Head direction cell is located in presubiculum to encode head direction information, it projects on stripe cell in shallow cortex of entorhinal cortex, stripe cells in entorhinal cortex is worked as the input of grid cells to encode linear velocity information, grid cells proceeds path integral for inputting information of stripe cells, specific position codes and place field of place cells are formed through feature extraction. Place field is the key factors to build the cognitive map. Hippocampus has been considered as cognitive map structure of brain spatial environment representation, its' place cells, grid cells, and border cells in the entorhinal cortex, head direction cells in multiple encephalic regions, newly discovered stripe cells and various kinds of the sensory system which are formed the spatial navigation system inner brain.
Grid cells discharging and path integral of inner-source information is simulated by continuous attractor model, activity of spatial cells is derived from collective behavior of attractor neural network, the final state of network activity is: continuous steady state in low-dimensional flow pattern, these final steady state is attractor, position adjustment and update in flow pattern plate is derived from response of velocity of rat motion.
The present disclosure builds robot navigation map through combining hippocampus spatial cells of rat brain and color depth map collected by Kinect, attractor model is used as calculation model of spatial cells. Comparing with traditional SLAM navigation method, it requires low performance of hardware and sensor, it has better expandability and adaptability, the data fusion processing method used in the present disclosure is cost-effective compared with traditional Kalman filtering method, it builds a cognitive map of indoor and outdoor environment effectively and precisely.